OBJECTIVES: To investigate which were the most relevant sociodemographic and clinical variables associated with COVID-19 severity, and uncover how their inter-relations may have affected such severity. DESIGN: A retrospective observational study based on electronic health record data. PARTICIPANTS: Individuals ≥14 years old with a positive PCR or serology test, between 28 February and 31 May 2020, belonging to the Basque Country (Spain) public health system. Institutionalised and individuals admitted to a hospital at home unit were excluded from the study. MAIN OUTCOME MEASURE: Three severity categories were established: primary care, hospital/intensive care unit admission and death. RESULTS: A total of n=14 197 cases fulfilled the inclusion criteria. Most variables presented statistically significant associations with the outcome (p<0.0001). The Classification and Regression Trees recursive partitioning methodology (based on n=13 792) suggested that among all associations, those with, age, sex, stratification of patient healthcare complexity, chronic consumption of blood and blood-forming organ, and nervous system drugs, as well as the total number of chronic Anatomical Therapeutic Chemical types were the most relevant. Psychosis also emerged as a potential factor. CONCLUSIONS: Older cases are more likely to experience more severe outcomes. However, the sex, underlying health status and chronic drug consumption may interfere and alter the ageing effect. Understanding the factors related to the outcome severity is of key importance when designing and promoting public health intervention plans for the COVID-19 pandemic.

https://bmjopen.bmj.com/content/bmjopen/11/4/e049066.full.pdf

Variables associated with COVID-19 severity: an observational study of non-paediatric confirmed cases from the general population of the Basque Country, Spain.

https://doi.org/10.1016/j.gaceta.2022.03.006

Comparison of COVID-19 and non-COVID-19 papers

Les facteurs pronostiques dans la Covid-19

COVID-19 is having a dramatic impact on research and researchers. The pandemic has underlined the severity of known challenges in research and surfaced new ones, but also accelerated the adoption of innovations and manifested new opportunities. This review considers early trends emerging from meta-research on COVID-19. In particular, it focuses on the following topics: i) mapping COVID-19 research; ii) data and machine learning; iii) research practices including open access and open data, reviewing, publishing and funding; iv) communicating research to the public; v) the impact of COVID-19 on researchers, in particular with respect to gender and career trajectories. This overview finds that most early meta-research on COVID-19 has been reactive and focused on short-term questions, while more recently a shift to consider the long-term consequences of COVID-19 is taking place. Based on these findings, the author speculates that some aspects of doing research during COVID-19 are more likely to persist than others. These include: the shift to virtual for academic events such as conferences; the use of openly accessible pre-prints; the `datafication' of scholarly literature and consequent broader adoption of machine learning in science communication; the public visibility of research and researchers on social and online media.

Meta-research on COVID-19: An overview of the early trends

Escalas pronósticas de morbimortalidad por COVID-19: necesarias pero también fiables

Introduction The coronavirus disease 2019 (COVID-19) pandemic has been unprecedented in recent history. The rapid global spread has demonstrated how the emergence of a novel pathogen necessitates new information to advise both healthcare systems and policy-makers. The directives for the management of COVID-19 have been limited to infection control measures and treatment of patients, which has left physicians and researchers alone to navigate the massive amount of research being published while searching for evidence-based strategies to care for patients. To tackle this barrier, we launched CovidReview.ca, an open-access, continually updated, online platform that screens available COVID-19 research to determine higher quality publications. This paper uses data from this review process to explore the activity and trends of COVID-19 research worldwide over time, while specifically looking at the types of studies being published. Materials and Methods The literature search was conducted on PubMed. Search terms included "COVID-19", "severe acute respiratory syndrome coronavirus 2", "coronavirus 19", "SARS-COV-2", and "2019-nCoV". All articles captured by this strategy were reviewed by a minimum of two reviewers and categorized by type of research, relevant medical specialties, and type of publication. Criteria were developed to allow for inclusion or exclusion to the website. Due to the volume of research, only a level 1 (title and abstract) screen was performed. Results The time period for the analysis was January 17, 2020, to May 10, 2020. The total number of papers captured by the search criteria was 10,685, of which 2,742 were included on the website and 7,943 were excluded. The greatest increase in the types of studies over the 16 weeks was narrative review/expert opinion papers followed by case series/reports. Meta-analyses, systematic reviews, and randomized controlled trials remained the least published types of studies. Conclusions The surge of research that accompanied the COVID-19 pandemic is unparalleled in recent years. From our analysis, it is clear that case reports and narrative reviews were the most widely published, particularly in the earlier days of this pandemic. Continued research that falls higher on the evidence pyramid and is more applicable to clinical settings is warranted.

/pdf/evaluating-trends-in-covid-19-research-activity-in-early-4dzhloq994.pdf

Evaluating Trends in COVID-19 Research Activity in Early 2020: The Creation and Utilization of a Novel Open-Access Database

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Evaluating Trends in COVID-19 Research Activity in Early 2020: The Creation and Utilization of a Novel Open-Access Database